A Hybrid Optimization Algorithm Based on Bat and Cuckoo Search

Yan Ming Duan

doi:10.4028/www.scientific.net/AMR.926-930.2889

Paper Titles

2D-DOA Estimation for MIMO Radar on Monostatic L-Shaped Minimum-Redundancy Linear Arrays
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A 3D-FEA of Temporomandibular Joint with Reduced Curvature of Curve of Spee
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A Dynamic Replication Algorithm Based on Access Popularity in Cloud Storage Environment
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A Dynamic Sub-Array Divided Technique for Spherical Conformal Array Antenna Using K-Means Algorithm
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A Hybrid Optimization Algorithm Based on Bat and Cuckoo Search
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A Multiple Frequency Band Watermarking Algorithm Based on Stripe Image
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A New Data Stream Clustering Approach about Intrusion Detection
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A New Method of Image Fusion Based on Wavelet Transform
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A New SVM Active Learning Algorithm Based on KNN
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 926-930A Hybrid Optimization Algorithm Based on Bat and...

A Hybrid Optimization Algorithm Based on Bat and Cuckoo Search

Abstract:

In order to solve the problems of bat algorithm including low convergence accuracy, slow convergence velocity and easily falling into local optimization, this paper presents an improved bat algorithm based on differential evolution algorithm. The mutation, crossover and selection mechanism of differential evolution algorithm is introduced into bat algorithm, the bat algorithm lack of mutation mechanism has the variation mechanism, so as to enhance the diversity of bat algorithm, the population can avoid falling into local optimum, which enhances the ability of global optimization for bat algorithm. The Simulation results of three standard benchmark functions show that the improved algorithm can greatly improve the convergence precision, convergence speed and robustness, and can effectively discourage the premature convergence.